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split grouper and kernelize [pr] (#10854)
This commit is contained in:
George Hotz
@@ -78,7 +78,7 @@ print("******** third, the UOp ***********")
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from tinygrad.engine.realize import run_schedule
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from tinygrad.engine.schedule import create_schedule_with_vars
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from tinygrad.engine.grouper import get_kernelize_map
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from tinygrad.engine.kernelize import get_kernelize_map
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# allocate some values + load in values
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a = UOp.new_buffer(DEVICE, 1, dtypes.int32)
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@@ -2,7 +2,7 @@ import sys, onnx
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from tinygrad import Tensor, fetch, GlobalCounters
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from tinygrad.uop.ops import UOp
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from tinygrad.frontend.onnx import OnnxRunner
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from tinygrad.engine.grouper import get_kernelize_map
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from tinygrad.engine.kernelize import get_kernelize_map
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from tinygrad.engine.schedule import create_schedule_with_vars
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from tinygrad.engine.realize import run_schedule
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@@ -3,7 +3,7 @@
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import os, multiprocessing, logging, pickle, sqlite3, difflib, warnings, itertools
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from typing import Callable, Any
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from tinygrad.helpers import VERSION, Context, ContextVar, colored, db_connection, getenv, tqdm, to_function_name
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from tinygrad.engine.grouper import get_kernelize_map
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from tinygrad.engine.kernelize import get_kernelize_map
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from tinygrad.codegen.kernel import Kernel
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from tinygrad.uop.ops import UOp, Ops
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@@ -6,7 +6,7 @@ from tinygrad.uop.ops import UOp, Ops, sint, graph_rewrite
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.tensor import _to_np_dtype
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from tinygrad.engine.realize import Runner
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from tinygrad.engine.grouper import view_left
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from tinygrad.engine.kernelize import view_left
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from tinygrad.dtype import ConstType, DType
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from tinygrad.nn.state import get_parameters
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from tinygrad.helpers import T, unwrap, CI
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@@ -15,7 +15,7 @@ from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.uop.ops import PatternMatcher, UOp, Ops, GroupOp, UPat, graph_rewrite, track_rewrites
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from tinygrad.uop.symbolic import symbolic_simple
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from tinygrad.helpers import CI, DEBUG, FUSE_ARANGE, SPLIT_REDUCEOP, GlobalCounters, Context, getenv, all_same, temp
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from tinygrad.engine.grouper import view_left, view_right, sym, get_kernelize_map, Kernel, create_ast, merge_views, create_kernels
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from tinygrad.engine.kernelize import view_left, view_right, sym, get_kernelize_map, Kernel, create_ast, merge_views, create_kernels
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from tinygrad.engine.schedule import ScheduleItem, create_schedule_with_vars
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from tinygrad.engine.realize import CompiledRunner, run_schedule, lower_schedule
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@@ -10,7 +10,7 @@ from tinygrad.device import Buffer, Device
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from tinygrad.uop.ops import Ops, UOp, UPat, KernelInfo, exec_alu # noqa F401
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from tinygrad.uop.spec import spec
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from tinygrad.renderer import ProgramSpec
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from tinygrad.engine.grouper import fix_kernel_ops
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from tinygrad.engine.kernelize import fix_kernel_ops
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from tinygrad.engine.realize import CompiledRunner
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from tinygrad.codegen import full_rewrite
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from tinygrad.uop.symbolic import sym
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@@ -516,7 +516,7 @@ class TestIndexingOrdering(unittest.TestCase):
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class TestUPatHelpers(unittest.TestCase):
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def test_location(self):
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self.assertEqual(sym.patterns[-1][0].location[0].replace("\\", "/").split("/")[-1], "symbolic.py")
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self.assertEqual(fix_kernel_ops.patterns[0][0].location[0].replace("\\", "/").split("/")[-1], "grouper.py")
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self.assertEqual(fix_kernel_ops.patterns[0][0].location[0].replace("\\", "/").split("/")[-1], "kernelize.py")
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self.assertEqual(spec.patterns[0][0].location[0].replace("\\", "/").split("/")[-1], "spec.py")
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test_upat = UPat(Ops.CONST, dtypes.bool)
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self.assertEqual(test_upat.location[0].split("/")[-1], __file__.replace("\\", "/").split("/")[-1])
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@@ -1,7 +1,7 @@
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import unittest
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from tinygrad import Tensor
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from tinygrad.uop.ops import PatternMatcher, Ops, UPat, graph_rewrite, RewriteContext, UOp
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from tinygrad.engine.grouper import sym, merge_views
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from tinygrad.engine.kernelize import sym, merge_views
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class TestRewriteTrackedChildren(unittest.TestCase):
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@unittest.skip("track_children no longer supported")
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@@ -15,7 +15,7 @@ from tinygrad.helpers import DEBUG, TC_SELECT, TC_OPT, AMX
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.shape.view import strides_for_shape
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from tinygrad.codegen.lowerer import get_contraction
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from tinygrad.engine.grouper import view_left
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from tinygrad.engine.kernelize import view_left
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from tinygrad.codegen import full_rewrite
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class KernelOptError(Exception): pass
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@@ -1,116 +1,10 @@
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from dataclasses import dataclass
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from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewrite, graph_rewrite_map, identity_element, resolve, can_pad, sint
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from tinygrad.uop.ops import track_rewrites, _substitute
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from tinygrad.uop.spec import type_verify, tensor_uop_spec
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from tinygrad.codegen.lowerer import get_contraction_with_reduce
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from tinygrad.uop.symbolic import symbolic_simple
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from tinygrad.helpers import Metadata, all_int, all_same, colored, prod, dedup, unwrap, getenv, pluralize
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from tinygrad.helpers import FUSE_CONV_BW, FUSE_ARANGE, DEBUG, DONT_REALIZE_EXPAND, DONT_GROUP_REDUCES, SPLIT_REDUCEOP
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from tinygrad.dtype import ImageDType
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from tinygrad.engine.multi import multi_pm
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from tinygrad.uop.ops import Ops, UOp, resolve, can_pad, GroupOp, UPat, PatternMatcher, graph_rewrite
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from tinygrad.helpers import all_int, prod, unwrap, dedup, DONT_REALIZE_EXPAND, DONT_GROUP_REDUCES, FUSE_CONV_BW
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.shape.view import View, strides_for_shape
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# creation can recurse a lot
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import sys
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sys.setrecursionlimit(10000)
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# **** schedule simplifier
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def simplify_stride0_reduce(reduce:UOp, x:UOp):
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# must be unmasked (NOTE: can be relaxed if not masked on stride 0 axis)
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if any(v.mask is not None for v in unwrap(x.st).views): return None
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# must have all stride 0 in the relevant axis (NOTE: can do partial)
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if not all(unwrap(x.st).views[-1].strides[axis] == 0 for axis in reduce.arg[1]) or not all_int(x.shape): return None
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prshape = prod(x.shape[i] for i in reduce.arg[1])
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ret = x.shrink(tuple((0,s) if i not in reduce.arg[1] else (0,1) for i,s in enumerate(x.shape)))
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match reduce.arg[0]:
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case Ops.ADD: return ret*prshape
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case Ops.MUL: return ret.pow(prshape)
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case Ops.MAX: return ret # NOTE: Ops.MAX is passthrough
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def split_reduceop(reduce:UOp, x:UOp):
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if not SPLIT_REDUCEOP or not all_int(x.shape) or (prod(x.shape)//prod(reduce.shape))<getenv("REDUCEOP_SPLIT_THRESHOLD", 32768): return None
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# if there are few globals, make some reduces into globals by splitting into two kernels
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# cap output buffer to 2**22: heuristic number of global outputs to achieve max occupancy with enough locals+upcasts for gemm
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# ~2**10 should be enough if GROUP is used
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# 256 split maximum should be "negligible reduce" for low prod(reduce.shape), 8 split minimum.
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# split is moved to the end to provide maximum locality for the second phase reduce.
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real_strides = unwrap(x.st).real_strides(ignore_valid=True)
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if not (split_candidates:=[(i,d) for i in reduce.arg[1] for d in range(min(256,2**getenv("REDUCEOP_SPLIT_SIZE",22)//prod(reduce.shape)),8-1,-1)
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if x.shape[i]%d==0 and real_strides[i]!=0]): return None
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dim_to_split, divisor = split_candidates[0]
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splitted_shape = x.shape[:dim_to_split]+(divisor,)+(x.shape[dim_to_split]//divisor,)+x.shape[dim_to_split+1:]
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splitted = x.reshape(splitted_shape).permute(tuple([d for d in range(len(splitted_shape)) if d!=dim_to_split]+[dim_to_split]))
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if DEBUG >= 3: print(f"split {divisor}: {x.shape} -> {splitted.shape} -> {reduce.shape}")
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# reduce original axes, then split
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return splitted.r(*reduce.arg).r(reduce.arg[0], (len(reduce.shape),)).reshape(reduce.shape)
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def copy_reorder_view(copy:UOp, view:UOp, base:UOp):
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if prod(view.shape) < prod(base.shape): return view.contiguous().copy_to_device(copy.device)
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return base.copy_to_device(copy.device).view(view.arg)
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ALWAYS_CONTIGUOUS = {Ops.CONTIGUOUS, Ops.ASSIGN, Ops.COPY, Ops.BUFFER, Ops.BUFFER_VIEW,
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Ops.CONST, Ops.BIND, Ops.DEVICE, Ops.MSELECT, Ops.MSTACK, Ops.GBARRIER}
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sym = symbolic_simple+PatternMatcher([
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# UOp with size 0 is zero
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(UPat(GroupOp.All-{Ops.SINK}, name="root"), lambda root: root.const_like(0) if root.base.st is not None and root.size == 0 \
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and not (root.base.op is Ops.CONST and root.base.arg == 0) else None),
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# DETACH and CONTIGUOUS_BACKWARD are NOOPs here
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(UPat((Ops.DETACH, Ops.CONTIGUOUS_BACKWARD), name="x"), lambda x: x.src[0]),
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# reduce of size 0 is the identity element
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(UPat(Ops.REDUCE_AXIS, name="reduce", src=(UPat.var("x"),)),
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lambda reduce,x: reduce.const_like(identity_element(reduce.arg[0], reduce.dtype)) if x.size == 0 and reduce.size != 0 else None),
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# reduce on stride 0 is collapsed
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(UPat(Ops.REDUCE_AXIS, name="reduce", src=(UPat.var("x"),)), simplify_stride0_reduce),
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# split_reduceop
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(UPat(Ops.REDUCE_AXIS, name="reduce", src=(UPat.var("x"),)), split_reduceop),
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# COPY(CONST) creates a new CONST on the destination device
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(UPat(Ops.COPY, name="root", src=(UPat.cvar("x"), UPat(Ops.DEVICE))), lambda root,x: root.const_like(x.arg)),
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# non device changing COPY is a NOOP
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(UPat(Ops.COPY, name="c", src=(UPat.var("x"), UPat(Ops.DEVICE))), lambda c,x: x if c.device == x.device else None),
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# store a shrink before COPY, otherwise view after the COPY
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(UPat(Ops.COPY, src=(UPat(Ops.VIEW, src=(UPat.var("base"),), name="view"), UPat(Ops.DEVICE)), name="copy"), copy_reorder_view),
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# remove cast to image when it's already a contiguous image
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(UPat(Ops.CAST, name="cast", src=(UPat(Ops.VIEW, name="vm", src=(UPat(Ops.CONTIGUOUS, name="base"),)),)),
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lambda cast,base,vm: base.view(vm.st) if isinstance(cast.dtype, ImageDType) and isinstance(base.dtype, ImageDType) else None),
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# CAST before masking constants
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(UPat.cvar("x").view().cast(name="c"), lambda x,c: x.cast(c.dtype).view(c.src[0].arg)),
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# make things that can't be images not images
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(UPat(GroupOp.All-{Ops.BUFFER, Ops.VIEW, Ops.CONST, Ops.DEVICE}, name="u"), lambda u: u.replace(dtype=dt.base) if isinstance(dt:=u.dtype,ImageDType)
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and (prod(u.shape) != prod(dt.shape) or not any(u.shape[x]%4 == 0 for x in u.st.unit_stride_axes())) else None),
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# remove contiguous if we can just view the buffer
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(UPat(Ops.CONTIGUOUS, name="root", src=(UPat(Ops.VIEW, name="view", src=(UPat(Ops.BUFFER, name="buf"),)),)),
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lambda root,view,buf: view if view.st.contiguous and view.size == buf.size else None),
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# contiguous/buffer/copy/assign is already contiguous
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(UPat(Ops.CONTIGUOUS, name="root", src=(UPat((Ops.CONTIGUOUS, Ops.BUFFER, Ops.COPY, Ops.ASSIGN)),)), lambda root: root.src[0]),
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# substitute BITCAST/CONTIGUOUS with BUFFER_VIEW on DISK
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(UPat((Ops.BITCAST, Ops.CONTIGUOUS), src=(UPat.var("x"),), name="t"), lambda x,t: UOp(Ops.BUFFER_VIEW, t.dtype, (x.base,),
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(t.size, x.st.views[0].offset)).reshape(t.shape) if isinstance(x.device, str) and x.device.startswith("DISK") else None),
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# double ASSIGN to same target is one ASSIGN
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(UPat(Ops.ASSIGN, src=(UPat.var("t"), UPat(Ops.ASSIGN, src=(UPat.var("t"), UPat.var("x"))))), lambda x,t: t.assign(x.contiguous())),
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# ASSIGN to unrealized replaces the UOp
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(UPat(Ops.ASSIGN, src=(UPat.var("t"), UPat.var("x"))), lambda x,t: x.contiguous() if t.base.op not in {Ops.BUFFER, Ops.BUFFER_VIEW} and
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not (t.base.op is Ops.MSTACK and all(x.op is Ops.BUFFER for x in t.base.src)) else None),
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# put CAST to smaller dtype before EXPAND
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(UPat(Ops.CAST, name="cast", src=(UPat(Ops.VIEW, name="vm"),)), lambda cast,vm: vm.base.cast(cast.dtype).view(vm.st)
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if cast.dtype.itemsize <= vm.dtype.itemsize and resolve(prod(vm.shape) > vm.st.real_size()) else None),
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# put UnaryOps before EXPANDs, if it can fuse with the input
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(UPat(GroupOp.Unary, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="inp"),), name="v"),), name="alu"),
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lambda inp,v,alu: inp.alu(alu.op).view(v.st) if resolve(prod(alu.shape) > v.st.real_size()) else None),
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])
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# support for using a contiguous permuted view instead of the parent view if one exists
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def found_contiguous(ctx:dict[UOp, UOp], contig:UOp, src:UOp):
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if (sti:=unwrap(src.st).invert(src.base.shape)) is not None: ctx[src.base] = contig.view(sti)
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replace_contiguous = PatternMatcher([
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(UPat(Ops.CONTIGUOUS, src=(UPat(Ops.VIEW, name="src"),), name="contig"), found_contiguous),
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(UPat(GroupOp.ALU, name="alu"), lambda ctx,alu: alu.replace(src=new_src) if (new_src:=tuple(ctx.get(s, s) for s in alu.src)) != alu.src else None),
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])
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# **** Grouper decides which of the UOps realize
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def realize(ctx:dict[UOp, None], tr:UOp) -> None: ctx[tr] = None
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@@ -222,363 +116,3 @@ def group_realizes(sink:UOp) -> dict[UOp, None]:
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top_reduce = reduceop.src[0].base
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if len(children.get(top_reduce, {})) == 1: del realizes[top_reduce]
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return realizes
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# **** create kernels
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@dataclass(frozen=True)
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class Kernel:
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ast: UOp
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metadata: tuple[Metadata, ...] = ()
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def __repr__(self):
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ast_rep = f"SINK{tuple(s.op for s in self.ast.src)}" if self.ast.op is Ops.SINK else repr(self.ast.op)
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return f"<Kernel {len(list(self.ast.toposort()))} {ast_rep} {self.metadata}>"
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def create_kernel(x:UOp, b:UOp|None=None):
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if b is None: b = UOp.new_buffer(x.device, x.size, x.dtype)
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kernel = UOp(Ops.KERNEL, src=(b,)+x.src, arg=Kernel(x.sink(), m if (m:=x.metadata) else ()))
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buffer = b.base if b.size == b.base.size else UOp(Ops.BUFFER_VIEW, b.dtype, (b.base,), (b.size, b.arg.views[0].offset))
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return buffer.assign(kernel).reshape(x.shape)
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DONT_PLACE_IN_KERNEL = {Ops.KERNEL, Ops.ASSIGN, Ops.BUFFER, Ops.MSELECT, Ops.MSTACK, Ops.MULTI}
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def append_to_kernel(x:UOp):
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new_srcs: list[UOp] = []
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metadata = x.arg.metadata
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for s in x.src:
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if s.op in DONT_PLACE_IN_KERNEL: new_srcs.append(s)
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else:
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new_srcs.extend(s.src)
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# NOTE: because const and device are shared UOps they don't change metadata
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# NOTE: if it's a reshape after ASSIGN we're not fusing that parent kernel
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if s.base.op not in {Ops.CONST, Ops.DEVICE} and (not (s.op is Ops.RESHAPE and s.base.op is Ops.ASSIGN)) and (m:=s.metadata): metadata += m
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if (new_src:=tuple(dedup(new_srcs))) != x.src: return x.replace(src=new_src, arg=Kernel(x.arg.ast, tuple(dedup(metadata))))
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create_kernels = PatternMatcher([
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# always give assign/gbarrier a kernel
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(UPat.assign(UPat.var("b"), UPat(GroupOp.All-{Ops.KERNEL}), name="x"), create_kernel),
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(UPat(Ops.GBARRIER, src=(UPat.var("x"),)), create_kernel),
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# walk back the local graph until we reach a realized source
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(UPat(Ops.KERNEL, name="x"), append_to_kernel),
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# push RESHAPE through MSELECT
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(UPat(Ops.MSELECT, src=(UPat(Ops.RESHAPE, name="r"),), name="ms"), lambda ms,r: r.src[0].mselect(ms.arg).reshape(r.arg)),
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# push RESHAPE through MSTACK
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(UPat(Ops.MSTACK, src=UPat(Ops.RESHAPE), name="ms"),
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lambda ms: UOp(Ops.MSTACK, ms.dtype, tuple(x.src[0] for x in ms.src)).reshape(ms.src[0].arg)),
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])
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# **** swizzler
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merge_views = PatternMatcher([
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# merge adjacent views
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(UPat(Ops.VIEW, src=(UPat(Ops.VIEW, name="v1"),), name="v2"), lambda v1,v2: v1.replace(arg=v1.arg+v2.arg)),
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# replace MovementOps with VIEW
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(UPat(GroupOp.Movement, src=(UPat.var("x"),), name="mop"), lambda mop,x: x.base.view(mop.st)),
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# remove NOOP views
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(UPat.var("x").view(name="view"), lambda x,view: x if x.st is not None and view.st.contiguous and view.shape == x.shape else None),
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(UPat(GroupOp.All-{Ops.DEFINE_GLOBAL}).view(name="view"),
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lambda view: view.const_like(0) if (mask:=view.st.views[-1].mask) is not None and any((x[1]-x[0]) == 0 for x in mask) else None),
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# only unmaksed VIEW on CONST replaces the ShapeTracker
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(UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="x"),), name="view"),
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lambda x,view: x.replace(src=(x.src[0].replace(arg=x.st+view.st),)) if all(v.mask is None for v in (x.st+view.st).views) else None),
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])
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def reduce_push_add_ones(src:UOp, r:UOp, view:UOp):
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# contiguous, expand, and the same with ones removed
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if unwrap(view.st).contiguous and len(r.shape) < len(view.shape) and \
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tuple(x for x in r.shape if resolve(x != 1)) == tuple(x for x in view.shape if resolve(x != 1)):
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new_shape: list[sint] = []
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new_reduce_axis = []
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if (contraction:=get_contraction_with_reduce(view.shape, r.shape, r.arg[1])) is None: return None
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for i,pairs in enumerate(contraction):
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new_shape_chunk = [view.shape[p] for p in pairs]
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if i in r.arg[1]:
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# if this is a reduce axis, we need a 1 in the view here to put it
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assert len(new_shape_chunk) > 0
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new_shape += [1]*(len(pairs)-1) + [src.shape[i]]
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new_reduce_axis.append(len(new_shape)-1)
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else:
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# otherwise, pass through the new_shape_chunk
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new_shape += new_shape_chunk
|
||||
ret = r.replace(src=(src.reshape(tuple(new_shape)),), arg=(r.arg[0], tuple(new_reduce_axis))+r.arg[2:])
|
||||
assert ret.shape == view.shape, f"shape mismatch on reduce_push_add_ones, {ret.shape} != {view.shape}"
|
||||
return ret
|
||||
return None
|
||||
|
||||
view_left = merge_views+PatternMatcher([
|
||||
# view before elementwise and buffer ops
|
||||
(UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.BIND, Ops.LOAD, Ops.STORE, Ops.VALID}, name="e"),), name="view"),
|
||||
lambda e,view: e.replace(src=tuple(s.view(view.st) for s in e.src))),
|
||||
# if there's ones added after reduce, put this before the reduce
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), reduce_push_add_ones),
|
||||
])
|
||||
|
||||
def apply_swizzle(u:UOp) -> UOp: return graph_rewrite(u, view_left, name="Sub View Left")
|
||||
|
||||
# change reduceop axes and input ShapeTrackers, view gets replaced with a reshape.
|
||||
def swizzle_reduceop(r:UOp, src:UOp, view:UOp, fuse=False):
|
||||
# contiguous and same size can push to children
|
||||
# if there's a reduce child, shapes match with ones removed
|
||||
if unwrap(view.st).contiguous and view.size == r.size and \
|
||||
(not (len(r.arg) == 3 and r.arg[2]) or # arg[2] = True is fuse marker
|
||||
tuple((i,x) for i,x in enumerate(r.shape) if resolve(x != 1)) == tuple((i,x) for i,x in enumerate(view.shape) if resolve(x != 1))):
|
||||
return None
|
||||
# swizzle the input
|
||||
input_st = ShapeTracker.from_shape(src.shape)
|
||||
tmp = input_st.permute(tuple(i for i in range(len(input_st.shape)) if i not in r.axis_arg)+r.axis_arg)
|
||||
prshape = prod(rshape:=tmp.shape[-len(r.axis_arg):])
|
||||
strides = strides_for_shape(rshape)
|
||||
nv = [View.create(v.shape+rshape, tuple(x*prshape for x in v.strides)+strides,
|
||||
v.offset*prshape, v.mask+tuple((0,s) for s in rshape) if v.mask is not None else None) for v in unwrap(view.st).views]
|
||||
new_view = tmp + ShapeTracker(tuple(nv))
|
||||
swizzled_input = apply_swizzle(src.view(new_view))
|
||||
# create a new reduceop
|
||||
new_axis = tuple(range(len(view.shape), len(view.shape) + len(r.axis_arg)))
|
||||
if fuse: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input.fuse(),), (r.arg[0], new_axis, True))
|
||||
else: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input,), (r.arg[0], new_axis))
|
||||
return red.reshape(view.shape)
|
||||
|
||||
def reduceop_view_right(src:UOp, v:UOp, r:UOp):
|
||||
assert unwrap(v.st).contiguous and v.size == src.size, f"can't compute new axis for {src.shape} -> {r.shape}"
|
||||
new_axis = [i for i,(s,u) in enumerate(zip(src.shape, r.shape)) if s != u]
|
||||
return src.r(r.arg[0], tuple(new_axis)).reshape(r.shape)
|
||||
|
||||
def elementwise_view_right(root:UOp):
|
||||
if not (swizzles:=[x for x in root.src if x.op is Ops.VIEW and x.base.op not in ALWAYS_CONTIGUOUS]): return None
|
||||
assert all_same([x.base.size for x in swizzles]), f"swizzle inputs must have the same size {swizzles}"
|
||||
# place view after applying the elementwise op
|
||||
new_st = ShapeTracker.from_shape(swizzles[0].base.shape)
|
||||
new_src = [x.base if x.base.shape==new_st.shape else apply_swizzle(x.view(new_st)) for x in root.src]
|
||||
# reshape to match downstream shapes
|
||||
return root.replace(src=tuple(new_src)).reshape(root.shape)
|
||||
|
||||
# push VIEW to children
|
||||
view_right = merge_views+PatternMatcher([
|
||||
# push a non contiguous ShapeTracker through reduceop
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), swizzle_reduceop),
|
||||
# apply view after reduceops
|
||||
(UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="src"),), name="v"),), name="r"), reduceop_view_right),
|
||||
# apply view after elementwise ops
|
||||
(UPat(GroupOp.All-{Ops.SINK, Ops.GBARRIER}, name="root"), elementwise_view_right),
|
||||
# merge axes for double reduce (invert of SPLIT_REDUCEOP=1)
|
||||
(UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.REDUCE_AXIS, name="r1"),), name="r2"),
|
||||
lambda r1,r2: r1.replace(arg=(r1.arg[0], r2.arg[1]+r1.arg[1])) if r1.arg[0] is r2.arg[0] else None),
|
||||
])
|
||||
|
||||
# **** fix kernel AST
|
||||
|
||||
add_buffer_ops = PatternMatcher([
|
||||
# LOAD
|
||||
(UPat(Ops.BUFFER, name="x"), lambda ctx,x: UOp.load(UOp(Ops.DEFINE_GLOBAL, x.dtype.ptr(x.size), (), ctx.index(x)).view(x.st),)),
|
||||
# STORE (except for meta ops)
|
||||
(UPat(Ops.SINK, src=(UPat(GroupOp.Meta, name="x"),)), lambda x:x),
|
||||
(UPat(Ops.SINK, src=UPat(GroupOp.All-{Ops.STORE}), name="sink"), lambda ctx,sink:
|
||||
UOp.sink(*[UOp.store(UOp(Ops.DEFINE_GLOBAL, (s:=x.base).dtype.ptr(ctx[i].size), (), i).view(s.st), s) for i,x in enumerate(sink.src)])),
|
||||
# passthrough ASSIGN
|
||||
(UPat(Ops.ASSIGN, name="x"), lambda x: x.src[1]),
|
||||
# VALID
|
||||
(UPat(Ops.VIEW, src=(UPat.cvar(),), name="self"), UOp.valid),
|
||||
])
|
||||
|
||||
def check_load_st(glbl:UOp, view:UOp):
|
||||
if glbl.arg != 0 or (st:=unwrap(view.st)).contiguous: return
|
||||
# if it has a single view and it becomes contiguous when you shrink expanded axes, it's fine
|
||||
if len(st.views) == 1 and st.shrink(tuple((0,1) if st == 0 else (0,s) for s,st in zip(st.shape, st.views[0].strides))).contiguous: return
|
||||
# if it has a single view and it's equal when you shrink a contig, it's fine
|
||||
if len(st.views) == 1 and (mask:=st.views[0].mask) is not None and ShapeTracker.from_shape(st.shape).shrink(mask) == st.shrink(mask): return
|
||||
# otherwise, it's not fine
|
||||
raise RuntimeError("self operand of augmented assign must be contiguous.\nhelp: consider using .contiguous():\n"
|
||||
+colored(" - a += a.T\n", "red")+colored(" + a += a.T.contiguous()", "green"))
|
||||
|
||||
fix_kernel_ops = PatternMatcher([
|
||||
# remove CONTIGUOUS/DEVICE from kernel AST
|
||||
(UPat((Ops.CONTIGUOUS, Ops.MSELECT), src=(UPat.var("x"),)), lambda x: x),
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.DEVICE),), name="view"), lambda view: view.replace(src=())),
|
||||
# no ImageDType after index
|
||||
(UPat(GroupOp.All-{Ops.DEFINE_GLOBAL, Ops.VIEW}, name="x"), lambda x: x.replace(dtype=x.dtype.base) if isinstance(x.dtype, ImageDType) else None),
|
||||
# if this kernel also assigns to the loaded buffer, ensure we can index it correctly
|
||||
(UPat(Ops.LOAD, src=(UPat.var("glbl").view(name="view"),)), check_load_st),
|
||||
])
|
||||
|
||||
replace_globals = PatternMatcher([
|
||||
# replace ASSIGN with the target BUFFER
|
||||
(UPat(Ops.ASSIGN, src=(UPat(Ops.BUFFER), UPat(Ops.KERNEL)), name="assign", allow_any_len=True), lambda assign: assign.src[0]),
|
||||
# HACK: select the 0 branch of MSTACK (the device is wrong after this, is that okay?)
|
||||
(UPat(Ops.MSTACK, name="x"), lambda x: x.src[0]),
|
||||
])
|
||||
|
||||
def fix_kernel_ast(k:UOp) -> UOp|None:
|
||||
if k.arg.ast.op in GroupOp.Meta or all(s.op is Ops.STORE for s in k.arg.ast.src): return None
|
||||
# replace global memory ops with the BUFFER they write to
|
||||
ast = graph_rewrite(k.arg.ast, replace_globals, bottom_up=True, name="replace globals")
|
||||
# push views to edges
|
||||
ast = graph_rewrite(graph_rewrite(ast, view_left, name="Main View Left"), view_right, name="Main View Right")
|
||||
# replace buffer with define_global + add load/store last
|
||||
bufs = []
|
||||
for s in k.src:
|
||||
s = s.buf_uop
|
||||
# traverse back through MSELECT and MSTACK. HACK: 0 branch of MSTACK only
|
||||
while s.op in {Ops.MSELECT, Ops.MSTACK}: s = s.src[0]
|
||||
bufs.append(s)
|
||||
ast = graph_rewrite(ast, view_left+add_buffer_ops+fix_kernel_ops, bufs, bottom_up=True, name="replace buffer")
|
||||
if ast.op is Ops.SINK and not all_same([x.device for x in k.src]):
|
||||
raise RuntimeError(f"all buffers must be on the same device: {tuple(b.buf_uop.buffer for b in k.src)}")
|
||||
return k.replace(arg=Kernel(ast, k.arg.metadata))
|
||||
|
||||
create_ast = PatternMatcher([(UPat(Ops.KERNEL, name="k"), fix_kernel_ast),])
|
||||
|
||||
# ** add metadata of KERNEL outputs
|
||||
|
||||
def append_metadata(root:UOp, k:UOp):
|
||||
if not root.metadata or (new_metadata:=tuple(dedup(k.arg.metadata+root.metadata))) == k.arg.metadata: return None
|
||||
return root.replace(src=(root.src[0], k.replace(arg=Kernel(k.arg.ast, new_metadata)))+root.src[2:])
|
||||
|
||||
replace_metadata = PatternMatcher([(UPat(Ops.ASSIGN, src=(UPat(), UPat(Ops.KERNEL, name="k")), name="root", allow_any_len=True), append_metadata),])
|
||||
|
||||
pm_fuse = PatternMatcher([
|
||||
# FUSE on CONTIGUOUS removes FUSE
|
||||
(UPat(Ops.CONTIGUOUS, name="c").fuse(), lambda c: c),
|
||||
|
||||
# FUSE triggers swizzle on reduceop
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r").or_casted(),), name="view").fuse(),
|
||||
lambda r,src,view: ret.cast(view.dtype) if (ret:=swizzle_reduceop(r, src, view, fuse=True)) is not None else None),
|
||||
|
||||
# FUSE on reduce (without view) adds fuse marker to grouper
|
||||
(UPat(Ops.REDUCE_AXIS, name="r").fuse(),
|
||||
lambda r: r.replace(src=(r.src[0].fuse(),), arg=r.arg+(True,)) if len(r.arg) == 2 else None),
|
||||
|
||||
# remove FUSE and insert CONTIGUOUS if it's an unsafe pad
|
||||
(UPat(Ops.VIEW, src=(UPat(GroupOp.UnsafePad, name="alu"),), name="view").fuse(),
|
||||
lambda alu, view: alu.contiguous().view(view.st) if any(v.mask is not None for v in view.st.views) else None),
|
||||
|
||||
# FUSE elementwise.
|
||||
(UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST}, name="alu"),), name="view").fuse(),
|
||||
lambda alu, view: alu.replace(src=tuple(apply_swizzle(x.view(view.arg)).fuse() for x in alu.src))),
|
||||
|
||||
# push FUSE through to srcs
|
||||
(UPat(Ops.FUSE, name="x"), lambda x: x.src[0].replace(src=tuple(y.fuse() for y in x.src[0].src))),
|
||||
])
|
||||
|
||||
def do_fusion(x:UOp):
|
||||
found_contiguous = {}
|
||||
def gate_contiguous(x):
|
||||
if is_contiguous:=(x.op is Ops.CONTIGUOUS): found_contiguous[x] = x.replace(src=(UOp(Ops.VIEW, arg=x.st),))
|
||||
return not is_contiguous
|
||||
x.toposort(gate=gate_contiguous)
|
||||
del gate_contiguous
|
||||
return graph_rewrite(x.substitute(found_contiguous), pm_fuse, name="local fusion").substitute({v:k for k,v in found_contiguous.items()})
|
||||
|
||||
def fuse_arange(root:UOp):
|
||||
# skip if root is arange
|
||||
if not FUSE_ARANGE or root.src[0].base.op is Ops.CONST: return None
|
||||
# gather all local aranges (including any fused ones)
|
||||
local_arange: list[UOp] = []
|
||||
def gate_reduce(u):
|
||||
if u.op is Ops.REDUCE_AXIS and u.src[0].base.op is Ops.CONST: local_arange.append(u)
|
||||
return u.op not in {*ALWAYS_CONTIGUOUS, Ops.REDUCE_AXIS} or u is root
|
||||
toposort = root.toposort(gate=gate_reduce)
|
||||
if not local_arange: return None
|
||||
# fuse the nearest expand child of arange
|
||||
local_children: dict[UOp, list[UOp]] = {}
|
||||
for u in toposort:
|
||||
for s in u.src: local_children.setdefault(s, []).append(u)
|
||||
fuse_rep: dict[UOp, UOp] = {}
|
||||
# skip if root depends on aranges with different ndims. This can be improved
|
||||
if any(len(set(dims)) > 1 for dims in zip(*[r.src[0].shape for r in local_arange])): return
|
||||
for r in local_arange:
|
||||
# skip if already fused
|
||||
if len(r.arg) > 2: continue
|
||||
q = list(local_children[r])
|
||||
while q:
|
||||
u = q.pop()
|
||||
if not (curr_children:=local_children.get(u, [])): continue
|
||||
for child in curr_children:
|
||||
other_paths = {s for s in child.toposort() if s.op in {Ops.REDUCE_AXIS, Ops.BUFFER} and s not in {root, r}}
|
||||
fuse_rep[child] = child.replace(src=tuple(s.fuse() if s is u else s for s in child.src))
|
||||
if other_paths: break
|
||||
else: q.extend(curr_children)
|
||||
return root.substitute(fuse_rep, name="fuse_arange") if fuse_rep else None
|
||||
|
||||
do_fuse = PatternMatcher([
|
||||
(UPat(Ops.FUSE, name="x"), do_fusion),
|
||||
(UPat(Ops.REDUCE_AXIS, name="root"), fuse_arange),
|
||||
])
|
||||
|
||||
add_gbarrier = PatternMatcher([(UPat(GroupOp.All-{Ops.GBARRIER, Ops.ASSIGN}, name="x"),
|
||||
lambda ctx,x: x.replace(tag=1).gbarrier() if x in ctx and x.tag is None else None)])
|
||||
|
||||
# TODO: get this from the device through GrouperOpts
|
||||
DEVICE_MAX_BUFS = {"METAL":32, "WEBGPU":8}
|
||||
|
||||
def limit_bufs(root:UOp):
|
||||
# check if backend has a buffer limit
|
||||
device = root.device if isinstance(root.device, str) else root.device[0].split(":")[0]
|
||||
if not (MAX_BUFS:=getenv("MAX_KERNEL_BUFFERS", DEVICE_MAX_BUFS.get(device, 0))): return None
|
||||
# count number of unique buffers flowing into this op
|
||||
bufs: set[UOp] = set()
|
||||
def gate_input(u:UOp):
|
||||
if (is_load:=(u.op in {Ops.BUFFER, Ops.GBARRIER, Ops.ASSIGN, Ops.MSTACK})): bufs.add(u)
|
||||
return not is_load
|
||||
root.toposort(gate=gate_input)
|
||||
# NOTE: this -1 is for the output buffer
|
||||
if len(bufs)>=MAX_BUFS-1:
|
||||
return root.replace(src=tuple(s if s.base in bufs else s.replace(tag=1).gbarrier() for s in root.src))
|
||||
|
||||
finalize_gbarrier = PatternMatcher([
|
||||
# if an op takes more than one input, check combined LOADs don't exceed device limits
|
||||
(UPat(set.union(GroupOp.Binary, GroupOp.Ternary), name="root"), limit_bufs),
|
||||
# merge gbarrier
|
||||
(UPat((Ops.GBARRIER, Ops.CONTIGUOUS), src=(UPat(Ops.GBARRIER),), name="x"), lambda x: x.src[0]),
|
||||
# add contiguous to VIEW before GBARRIER
|
||||
(UPat(Ops.GBARRIER, src=(UPat(Ops.VIEW,),), name="x"), lambda x: x.src[0].contiguous().gbarrier()),
|
||||
# remove gbarrier on constants without a contiguous
|
||||
(UPat(Ops.GBARRIER, src=(UPat(Ops.CONST),), name="x"), lambda x: x.src[0]),
|
||||
])
|
||||
|
||||
remove_tags = PatternMatcher([(UPat(GroupOp.All, name="x"), lambda x: x.replace(tag=None) if x.tag is not None else None)])
|
||||
|
||||
@track_rewrites(name_fxn=lambda big_sink,ret: f"Schedule {pluralize('Kernel',len([u for u in ret[big_sink].toposort() if u.op is Ops.KERNEL]))}")
|
||||
def get_kernelize_map(big_sink:UOp) -> dict[UOp, UOp]:
|
||||
# multi + merge_views + simplify
|
||||
tensor_map = graph_rewrite_map(big_sink, multi_pm+do_fuse+merge_views+sym+replace_contiguous, ctx={}, name="merge_views")
|
||||
|
||||
# display the cleaned up tensor graph
|
||||
if getenv("VIZ"): graph_rewrite(tensor_map[big_sink], PatternMatcher([]), name="View Tensor Graph")
|
||||
|
||||
# insert gbarriers in places determined by the realize map
|
||||
realize_map = group_realizes(tensor_map[big_sink])
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], add_gbarrier, realize_map, bottom_up=True, input_map=tensor_map, name="insert_gbarrier")
|
||||
# optionally reorder gbarriers or insert more (top down)
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], finalize_gbarrier, input_map=tensor_map, name="finalize_gbarrier")
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], remove_tags, input_map=tensor_map, name="remove_tags")
|
||||
|
||||
# TODO: move view_left/view_right here
|
||||
|
||||
# group into kernels (this is context-free)
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], create_kernels, input_map=tensor_map, name="create_kernels")
|
||||
|
||||
# if a kernel depends on a buffer, and that buffer is later assigned to, make the assign depend on the kernel's assign
|
||||
kernel_assign: dict[UOp, UOp] = {}
|
||||
assign_rep: dict[UOp, UOp] = {}
|
||||
for u in tensor_map[big_sink].toposort():
|
||||
if u.op is not Ops.ASSIGN: continue
|
||||
kernel_assign[u.buf_uop] = u
|
||||
for s in u.src[1].src:
|
||||
# TODO: this is probably broken for MSELECT/MSTACK
|
||||
if s.op is not Ops.BUFFER or s is u.buf_uop or (a:=kernel_assign.get(s)) is None: continue
|
||||
if any(x.op is Ops.ASSIGN and x.buf_uop is s for x in u.toposort()):
|
||||
raise RuntimeError(f"cycle detected in graph, kernel for {u.buf_uop} must either depend on ASSIGN or BUFFER")
|
||||
assign_rep[a] = kernel_assign[s] = a.replace(src=a.src+(u,))
|
||||
if assign_rep:
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], _substitute, ctx=assign_rep, bottom_up=True, input_map=tensor_map, name="fix_assign")
|
||||
|
||||
# finally, create the AST for kernels
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], create_ast+replace_metadata, bottom_up=True, input_map=tensor_map, name="create_ast")
|
||||
|
||||
# display the final graph
|
||||
sched_sink = tensor_map[big_sink]
|
||||
if getenv("VIZ"): graph_rewrite(sched_sink, PatternMatcher([]), name="View Kernel Graph")
|
||||
if getenv("VIZ"): graph_rewrite(sched_sink, PatternMatcher([]), name="View Memory Graph")
|
||||
|
||||
# verify Kernels match the spec
|
||||
if __debug__: type_verify(list(sched_sink.toposort()), tensor_uop_spec)
|
||||
|
||||
return tensor_map
|
||||
|
||||
469
tinygrad/engine/kernelize.py
Normal file
469
tinygrad/engine/kernelize.py
Normal file
@@ -0,0 +1,469 @@
|
||||
from dataclasses import dataclass
|
||||
from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewrite, graph_rewrite_map, identity_element, resolve, sint
|
||||
from tinygrad.uop.ops import track_rewrites, _substitute
|
||||
from tinygrad.uop.spec import type_verify, tensor_uop_spec
|
||||
from tinygrad.codegen.lowerer import get_contraction_with_reduce
|
||||
from tinygrad.uop.symbolic import symbolic_simple
|
||||
from tinygrad.helpers import Metadata, all_int, all_same, colored, prod, dedup, unwrap, getenv, pluralize, FUSE_ARANGE, DEBUG, SPLIT_REDUCEOP
|
||||
from tinygrad.dtype import ImageDType
|
||||
from tinygrad.engine.multi import multi_pm
|
||||
from tinygrad.shape.shapetracker import ShapeTracker
|
||||
from tinygrad.shape.view import View, strides_for_shape
|
||||
from tinygrad.engine.grouper import group_realizes, ALWAYS_CONTIGUOUS
|
||||
|
||||
# creation can recurse a lot
|
||||
import sys
|
||||
sys.setrecursionlimit(10000)
|
||||
|
||||
# **** schedule simplifier
|
||||
|
||||
def simplify_stride0_reduce(reduce:UOp, x:UOp):
|
||||
# must be unmasked (NOTE: can be relaxed if not masked on stride 0 axis)
|
||||
if any(v.mask is not None for v in unwrap(x.st).views): return None
|
||||
# must have all stride 0 in the relevant axis (NOTE: can do partial)
|
||||
if not all(unwrap(x.st).views[-1].strides[axis] == 0 for axis in reduce.arg[1]) or not all_int(x.shape): return None
|
||||
prshape = prod(x.shape[i] for i in reduce.arg[1])
|
||||
ret = x.shrink(tuple((0,s) if i not in reduce.arg[1] else (0,1) for i,s in enumerate(x.shape)))
|
||||
match reduce.arg[0]:
|
||||
case Ops.ADD: return ret*prshape
|
||||
case Ops.MUL: return ret.pow(prshape)
|
||||
case Ops.MAX: return ret # NOTE: Ops.MAX is passthrough
|
||||
|
||||
def split_reduceop(reduce:UOp, x:UOp):
|
||||
if not SPLIT_REDUCEOP or not all_int(x.shape) or (prod(x.shape)//prod(reduce.shape))<getenv("REDUCEOP_SPLIT_THRESHOLD", 32768): return None
|
||||
# if there are few globals, make some reduces into globals by splitting into two kernels
|
||||
# cap output buffer to 2**22: heuristic number of global outputs to achieve max occupancy with enough locals+upcasts for gemm
|
||||
# ~2**10 should be enough if GROUP is used
|
||||
# 256 split maximum should be "negligible reduce" for low prod(reduce.shape), 8 split minimum.
|
||||
# split is moved to the end to provide maximum locality for the second phase reduce.
|
||||
real_strides = unwrap(x.st).real_strides(ignore_valid=True)
|
||||
if not (split_candidates:=[(i,d) for i in reduce.arg[1] for d in range(min(256,2**getenv("REDUCEOP_SPLIT_SIZE",22)//prod(reduce.shape)),8-1,-1)
|
||||
if x.shape[i]%d==0 and real_strides[i]!=0]): return None
|
||||
dim_to_split, divisor = split_candidates[0]
|
||||
splitted_shape = x.shape[:dim_to_split]+(divisor,)+(x.shape[dim_to_split]//divisor,)+x.shape[dim_to_split+1:]
|
||||
splitted = x.reshape(splitted_shape).permute(tuple([d for d in range(len(splitted_shape)) if d!=dim_to_split]+[dim_to_split]))
|
||||
if DEBUG >= 3: print(f"split {divisor}: {x.shape} -> {splitted.shape} -> {reduce.shape}")
|
||||
# reduce original axes, then split
|
||||
return splitted.r(*reduce.arg).r(reduce.arg[0], (len(reduce.shape),)).reshape(reduce.shape)
|
||||
|
||||
def copy_reorder_view(copy:UOp, view:UOp, base:UOp):
|
||||
if prod(view.shape) < prod(base.shape): return view.contiguous().copy_to_device(copy.device)
|
||||
return base.copy_to_device(copy.device).view(view.arg)
|
||||
|
||||
sym = symbolic_simple+PatternMatcher([
|
||||
# UOp with size 0 is zero
|
||||
(UPat(GroupOp.All-{Ops.SINK}, name="root"), lambda root: root.const_like(0) if root.base.st is not None and root.size == 0 \
|
||||
and not (root.base.op is Ops.CONST and root.base.arg == 0) else None),
|
||||
# DETACH and CONTIGUOUS_BACKWARD are NOOPs here
|
||||
(UPat((Ops.DETACH, Ops.CONTIGUOUS_BACKWARD), name="x"), lambda x: x.src[0]),
|
||||
# reduce of size 0 is the identity element
|
||||
(UPat(Ops.REDUCE_AXIS, name="reduce", src=(UPat.var("x"),)),
|
||||
lambda reduce,x: reduce.const_like(identity_element(reduce.arg[0], reduce.dtype)) if x.size == 0 and reduce.size != 0 else None),
|
||||
# reduce on stride 0 is collapsed
|
||||
(UPat(Ops.REDUCE_AXIS, name="reduce", src=(UPat.var("x"),)), simplify_stride0_reduce),
|
||||
# split_reduceop
|
||||
(UPat(Ops.REDUCE_AXIS, name="reduce", src=(UPat.var("x"),)), split_reduceop),
|
||||
# COPY(CONST) creates a new CONST on the destination device
|
||||
(UPat(Ops.COPY, name="root", src=(UPat.cvar("x"), UPat(Ops.DEVICE))), lambda root,x: root.const_like(x.arg)),
|
||||
# non device changing COPY is a NOOP
|
||||
(UPat(Ops.COPY, name="c", src=(UPat.var("x"), UPat(Ops.DEVICE))), lambda c,x: x if c.device == x.device else None),
|
||||
# store a shrink before COPY, otherwise view after the COPY
|
||||
(UPat(Ops.COPY, src=(UPat(Ops.VIEW, src=(UPat.var("base"),), name="view"), UPat(Ops.DEVICE)), name="copy"), copy_reorder_view),
|
||||
# remove cast to image when it's already a contiguous image
|
||||
(UPat(Ops.CAST, name="cast", src=(UPat(Ops.VIEW, name="vm", src=(UPat(Ops.CONTIGUOUS, name="base"),)),)),
|
||||
lambda cast,base,vm: base.view(vm.st) if isinstance(cast.dtype, ImageDType) and isinstance(base.dtype, ImageDType) else None),
|
||||
# CAST before masking constants
|
||||
(UPat.cvar("x").view().cast(name="c"), lambda x,c: x.cast(c.dtype).view(c.src[0].arg)),
|
||||
# make things that can't be images not images
|
||||
(UPat(GroupOp.All-{Ops.BUFFER, Ops.VIEW, Ops.CONST, Ops.DEVICE}, name="u"), lambda u: u.replace(dtype=dt.base) if isinstance(dt:=u.dtype,ImageDType)
|
||||
and (prod(u.shape) != prod(dt.shape) or not any(u.shape[x]%4 == 0 for x in u.st.unit_stride_axes())) else None),
|
||||
# remove contiguous if we can just view the buffer
|
||||
(UPat(Ops.CONTIGUOUS, name="root", src=(UPat(Ops.VIEW, name="view", src=(UPat(Ops.BUFFER, name="buf"),)),)),
|
||||
lambda root,view,buf: view if view.st.contiguous and view.size == buf.size else None),
|
||||
# contiguous/buffer/copy/assign is already contiguous
|
||||
(UPat(Ops.CONTIGUOUS, name="root", src=(UPat((Ops.CONTIGUOUS, Ops.BUFFER, Ops.COPY, Ops.ASSIGN)),)), lambda root: root.src[0]),
|
||||
# substitute BITCAST/CONTIGUOUS with BUFFER_VIEW on DISK
|
||||
(UPat((Ops.BITCAST, Ops.CONTIGUOUS), src=(UPat.var("x"),), name="t"), lambda x,t: UOp(Ops.BUFFER_VIEW, t.dtype, (x.base,),
|
||||
(t.size, x.st.views[0].offset)).reshape(t.shape) if isinstance(x.device, str) and x.device.startswith("DISK") else None),
|
||||
# double ASSIGN to same target is one ASSIGN
|
||||
(UPat(Ops.ASSIGN, src=(UPat.var("t"), UPat(Ops.ASSIGN, src=(UPat.var("t"), UPat.var("x"))))), lambda x,t: t.assign(x.contiguous())),
|
||||
# ASSIGN to unrealized replaces the UOp
|
||||
(UPat(Ops.ASSIGN, src=(UPat.var("t"), UPat.var("x"))), lambda x,t: x.contiguous() if t.base.op not in {Ops.BUFFER, Ops.BUFFER_VIEW} and
|
||||
not (t.base.op is Ops.MSTACK and all(x.op is Ops.BUFFER for x in t.base.src)) else None),
|
||||
# put CAST to smaller dtype before EXPAND
|
||||
(UPat(Ops.CAST, name="cast", src=(UPat(Ops.VIEW, name="vm"),)), lambda cast,vm: vm.base.cast(cast.dtype).view(vm.st)
|
||||
if cast.dtype.itemsize <= vm.dtype.itemsize and resolve(prod(vm.shape) > vm.st.real_size()) else None),
|
||||
# put UnaryOps before EXPANDs, if it can fuse with the input
|
||||
(UPat(GroupOp.Unary, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="inp"),), name="v"),), name="alu"),
|
||||
lambda inp,v,alu: inp.alu(alu.op).view(v.st) if resolve(prod(alu.shape) > v.st.real_size()) else None),
|
||||
])
|
||||
|
||||
# support for using a contiguous permuted view instead of the parent view if one exists
|
||||
|
||||
def found_contiguous(ctx:dict[UOp, UOp], contig:UOp, src:UOp):
|
||||
if (sti:=unwrap(src.st).invert(src.base.shape)) is not None: ctx[src.base] = contig.view(sti)
|
||||
|
||||
replace_contiguous = PatternMatcher([
|
||||
(UPat(Ops.CONTIGUOUS, src=(UPat(Ops.VIEW, name="src"),), name="contig"), found_contiguous),
|
||||
(UPat(GroupOp.ALU, name="alu"), lambda ctx,alu: alu.replace(src=new_src) if (new_src:=tuple(ctx.get(s, s) for s in alu.src)) != alu.src else None),
|
||||
])
|
||||
|
||||
# **** create kernels
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class Kernel:
|
||||
ast: UOp
|
||||
metadata: tuple[Metadata, ...] = ()
|
||||
def __repr__(self):
|
||||
ast_rep = f"SINK{tuple(s.op for s in self.ast.src)}" if self.ast.op is Ops.SINK else repr(self.ast.op)
|
||||
return f"<Kernel {len(list(self.ast.toposort()))} {ast_rep} {self.metadata}>"
|
||||
|
||||
def create_kernel(x:UOp, b:UOp|None=None):
|
||||
if b is None: b = UOp.new_buffer(x.device, x.size, x.dtype)
|
||||
kernel = UOp(Ops.KERNEL, src=(b,)+x.src, arg=Kernel(x.sink(), m if (m:=x.metadata) else ()))
|
||||
buffer = b.base if b.size == b.base.size else UOp(Ops.BUFFER_VIEW, b.dtype, (b.base,), (b.size, b.arg.views[0].offset))
|
||||
return buffer.assign(kernel).reshape(x.shape)
|
||||
|
||||
DONT_PLACE_IN_KERNEL = {Ops.KERNEL, Ops.ASSIGN, Ops.BUFFER, Ops.MSELECT, Ops.MSTACK, Ops.MULTI}
|
||||
def append_to_kernel(x:UOp):
|
||||
new_srcs: list[UOp] = []
|
||||
metadata = x.arg.metadata
|
||||
for s in x.src:
|
||||
if s.op in DONT_PLACE_IN_KERNEL: new_srcs.append(s)
|
||||
else:
|
||||
new_srcs.extend(s.src)
|
||||
# NOTE: because const and device are shared UOps they don't change metadata
|
||||
# NOTE: if it's a reshape after ASSIGN we're not fusing that parent kernel
|
||||
if s.base.op not in {Ops.CONST, Ops.DEVICE} and (not (s.op is Ops.RESHAPE and s.base.op is Ops.ASSIGN)) and (m:=s.metadata): metadata += m
|
||||
if (new_src:=tuple(dedup(new_srcs))) != x.src: return x.replace(src=new_src, arg=Kernel(x.arg.ast, tuple(dedup(metadata))))
|
||||
|
||||
create_kernels = PatternMatcher([
|
||||
# always give assign/gbarrier a kernel
|
||||
(UPat.assign(UPat.var("b"), UPat(GroupOp.All-{Ops.KERNEL}), name="x"), create_kernel),
|
||||
(UPat(Ops.GBARRIER, src=(UPat.var("x"),)), create_kernel),
|
||||
# walk back the local graph until we reach a realized source
|
||||
(UPat(Ops.KERNEL, name="x"), append_to_kernel),
|
||||
# push RESHAPE through MSELECT
|
||||
(UPat(Ops.MSELECT, src=(UPat(Ops.RESHAPE, name="r"),), name="ms"), lambda ms,r: r.src[0].mselect(ms.arg).reshape(r.arg)),
|
||||
# push RESHAPE through MSTACK
|
||||
(UPat(Ops.MSTACK, src=UPat(Ops.RESHAPE), name="ms"),
|
||||
lambda ms: UOp(Ops.MSTACK, ms.dtype, tuple(x.src[0] for x in ms.src)).reshape(ms.src[0].arg)),
|
||||
])
|
||||
|
||||
# **** swizzler
|
||||
|
||||
merge_views = PatternMatcher([
|
||||
# merge adjacent views
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.VIEW, name="v1"),), name="v2"), lambda v1,v2: v1.replace(arg=v1.arg+v2.arg)),
|
||||
# replace MovementOps with VIEW
|
||||
(UPat(GroupOp.Movement, src=(UPat.var("x"),), name="mop"), lambda mop,x: x.base.view(mop.st)),
|
||||
# remove NOOP views
|
||||
(UPat.var("x").view(name="view"), lambda x,view: x if x.st is not None and view.st.contiguous and view.shape == x.shape else None),
|
||||
(UPat(GroupOp.All-{Ops.DEFINE_GLOBAL}).view(name="view"),
|
||||
lambda view: view.const_like(0) if (mask:=view.st.views[-1].mask) is not None and any((x[1]-x[0]) == 0 for x in mask) else None),
|
||||
# only unmaksed VIEW on CONST replaces the ShapeTracker
|
||||
(UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="x"),), name="view"),
|
||||
lambda x,view: x.replace(src=(x.src[0].replace(arg=x.st+view.st),)) if all(v.mask is None for v in (x.st+view.st).views) else None),
|
||||
])
|
||||
|
||||
def reduce_push_add_ones(src:UOp, r:UOp, view:UOp):
|
||||
# contiguous, expand, and the same with ones removed
|
||||
if unwrap(view.st).contiguous and len(r.shape) < len(view.shape) and \
|
||||
tuple(x for x in r.shape if resolve(x != 1)) == tuple(x for x in view.shape if resolve(x != 1)):
|
||||
new_shape: list[sint] = []
|
||||
new_reduce_axis = []
|
||||
if (contraction:=get_contraction_with_reduce(view.shape, r.shape, r.arg[1])) is None: return None
|
||||
for i,pairs in enumerate(contraction):
|
||||
new_shape_chunk = [view.shape[p] for p in pairs]
|
||||
if i in r.arg[1]:
|
||||
# if this is a reduce axis, we need a 1 in the view here to put it
|
||||
assert len(new_shape_chunk) > 0
|
||||
new_shape += [1]*(len(pairs)-1) + [src.shape[i]]
|
||||
new_reduce_axis.append(len(new_shape)-1)
|
||||
else:
|
||||
# otherwise, pass through the new_shape_chunk
|
||||
new_shape += new_shape_chunk
|
||||
ret = r.replace(src=(src.reshape(tuple(new_shape)),), arg=(r.arg[0], tuple(new_reduce_axis))+r.arg[2:])
|
||||
assert ret.shape == view.shape, f"shape mismatch on reduce_push_add_ones, {ret.shape} != {view.shape}"
|
||||
return ret
|
||||
return None
|
||||
|
||||
view_left = merge_views+PatternMatcher([
|
||||
# view before elementwise and buffer ops
|
||||
(UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.BIND, Ops.LOAD, Ops.STORE, Ops.VALID}, name="e"),), name="view"),
|
||||
lambda e,view: e.replace(src=tuple(s.view(view.st) for s in e.src))),
|
||||
# if there's ones added after reduce, put this before the reduce
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), reduce_push_add_ones),
|
||||
])
|
||||
|
||||
def apply_swizzle(u:UOp) -> UOp: return graph_rewrite(u, view_left, name="Sub View Left")
|
||||
|
||||
# change reduceop axes and input ShapeTrackers, view gets replaced with a reshape.
|
||||
def swizzle_reduceop(r:UOp, src:UOp, view:UOp, fuse=False):
|
||||
# contiguous and same size can push to children
|
||||
# if there's a reduce child, shapes match with ones removed
|
||||
if unwrap(view.st).contiguous and view.size == r.size and \
|
||||
(not (len(r.arg) == 3 and r.arg[2]) or # arg[2] = True is fuse marker
|
||||
tuple((i,x) for i,x in enumerate(r.shape) if resolve(x != 1)) == tuple((i,x) for i,x in enumerate(view.shape) if resolve(x != 1))):
|
||||
return None
|
||||
# swizzle the input
|
||||
input_st = ShapeTracker.from_shape(src.shape)
|
||||
tmp = input_st.permute(tuple(i for i in range(len(input_st.shape)) if i not in r.axis_arg)+r.axis_arg)
|
||||
prshape = prod(rshape:=tmp.shape[-len(r.axis_arg):])
|
||||
strides = strides_for_shape(rshape)
|
||||
nv = [View.create(v.shape+rshape, tuple(x*prshape for x in v.strides)+strides,
|
||||
v.offset*prshape, v.mask+tuple((0,s) for s in rshape) if v.mask is not None else None) for v in unwrap(view.st).views]
|
||||
new_view = tmp + ShapeTracker(tuple(nv))
|
||||
swizzled_input = apply_swizzle(src.view(new_view))
|
||||
# create a new reduceop
|
||||
new_axis = tuple(range(len(view.shape), len(view.shape) + len(r.axis_arg)))
|
||||
if fuse: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input.fuse(),), (r.arg[0], new_axis, True))
|
||||
else: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input,), (r.arg[0], new_axis))
|
||||
return red.reshape(view.shape)
|
||||
|
||||
def reduceop_view_right(src:UOp, v:UOp, r:UOp):
|
||||
assert unwrap(v.st).contiguous and v.size == src.size, f"can't compute new axis for {src.shape} -> {r.shape}"
|
||||
new_axis = [i for i,(s,u) in enumerate(zip(src.shape, r.shape)) if s != u]
|
||||
return src.r(r.arg[0], tuple(new_axis)).reshape(r.shape)
|
||||
|
||||
def elementwise_view_right(root:UOp):
|
||||
if not (swizzles:=[x for x in root.src if x.op is Ops.VIEW and x.base.op not in ALWAYS_CONTIGUOUS]): return None
|
||||
assert all_same([x.base.size for x in swizzles]), f"swizzle inputs must have the same size {swizzles}"
|
||||
# place view after applying the elementwise op
|
||||
new_st = ShapeTracker.from_shape(swizzles[0].base.shape)
|
||||
new_src = [x.base if x.base.shape==new_st.shape else apply_swizzle(x.view(new_st)) for x in root.src]
|
||||
# reshape to match downstream shapes
|
||||
return root.replace(src=tuple(new_src)).reshape(root.shape)
|
||||
|
||||
# push VIEW to children
|
||||
view_right = merge_views+PatternMatcher([
|
||||
# push a non contiguous ShapeTracker through reduceop
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), swizzle_reduceop),
|
||||
# apply view after reduceops
|
||||
(UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="src"),), name="v"),), name="r"), reduceop_view_right),
|
||||
# apply view after elementwise ops
|
||||
(UPat(GroupOp.All-{Ops.SINK, Ops.GBARRIER}, name="root"), elementwise_view_right),
|
||||
# merge axes for double reduce (invert of SPLIT_REDUCEOP=1)
|
||||
(UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.REDUCE_AXIS, name="r1"),), name="r2"),
|
||||
lambda r1,r2: r1.replace(arg=(r1.arg[0], r2.arg[1]+r1.arg[1])) if r1.arg[0] is r2.arg[0] else None),
|
||||
])
|
||||
|
||||
# **** fix kernel AST
|
||||
|
||||
add_buffer_ops = PatternMatcher([
|
||||
# LOAD
|
||||
(UPat(Ops.BUFFER, name="x"), lambda ctx,x: UOp.load(UOp(Ops.DEFINE_GLOBAL, x.dtype.ptr(x.size), (), ctx.index(x)).view(x.st),)),
|
||||
# STORE (except for meta ops)
|
||||
(UPat(Ops.SINK, src=(UPat(GroupOp.Meta, name="x"),)), lambda x:x),
|
||||
(UPat(Ops.SINK, src=UPat(GroupOp.All-{Ops.STORE}), name="sink"), lambda ctx,sink:
|
||||
UOp.sink(*[UOp.store(UOp(Ops.DEFINE_GLOBAL, (s:=x.base).dtype.ptr(ctx[i].size), (), i).view(s.st), s) for i,x in enumerate(sink.src)])),
|
||||
# passthrough ASSIGN
|
||||
(UPat(Ops.ASSIGN, name="x"), lambda x: x.src[1]),
|
||||
# VALID
|
||||
(UPat(Ops.VIEW, src=(UPat.cvar(),), name="self"), UOp.valid),
|
||||
])
|
||||
|
||||
def check_load_st(glbl:UOp, view:UOp):
|
||||
if glbl.arg != 0 or (st:=unwrap(view.st)).contiguous: return
|
||||
# if it has a single view and it becomes contiguous when you shrink expanded axes, it's fine
|
||||
if len(st.views) == 1 and st.shrink(tuple((0,1) if st == 0 else (0,s) for s,st in zip(st.shape, st.views[0].strides))).contiguous: return
|
||||
# if it has a single view and it's equal when you shrink a contig, it's fine
|
||||
if len(st.views) == 1 and (mask:=st.views[0].mask) is not None and ShapeTracker.from_shape(st.shape).shrink(mask) == st.shrink(mask): return
|
||||
# otherwise, it's not fine
|
||||
raise RuntimeError("self operand of augmented assign must be contiguous.\nhelp: consider using .contiguous():\n"
|
||||
+colored(" - a += a.T\n", "red")+colored(" + a += a.T.contiguous()", "green"))
|
||||
|
||||
fix_kernel_ops = PatternMatcher([
|
||||
# remove CONTIGUOUS/DEVICE from kernel AST
|
||||
(UPat((Ops.CONTIGUOUS, Ops.MSELECT), src=(UPat.var("x"),)), lambda x: x),
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.DEVICE),), name="view"), lambda view: view.replace(src=())),
|
||||
# no ImageDType after index
|
||||
(UPat(GroupOp.All-{Ops.DEFINE_GLOBAL, Ops.VIEW}, name="x"), lambda x: x.replace(dtype=x.dtype.base) if isinstance(x.dtype, ImageDType) else None),
|
||||
# if this kernel also assigns to the loaded buffer, ensure we can index it correctly
|
||||
(UPat(Ops.LOAD, src=(UPat.var("glbl").view(name="view"),)), check_load_st),
|
||||
])
|
||||
|
||||
replace_globals = PatternMatcher([
|
||||
# replace ASSIGN with the target BUFFER
|
||||
(UPat(Ops.ASSIGN, src=(UPat(Ops.BUFFER), UPat(Ops.KERNEL)), name="assign", allow_any_len=True), lambda assign: assign.src[0]),
|
||||
# HACK: select the 0 branch of MSTACK (the device is wrong after this, is that okay?)
|
||||
(UPat(Ops.MSTACK, name="x"), lambda x: x.src[0]),
|
||||
])
|
||||
|
||||
def fix_kernel_ast(k:UOp) -> UOp|None:
|
||||
if k.arg.ast.op in GroupOp.Meta or all(s.op is Ops.STORE for s in k.arg.ast.src): return None
|
||||
# replace global memory ops with the BUFFER they write to
|
||||
ast = graph_rewrite(k.arg.ast, replace_globals, bottom_up=True, name="replace globals")
|
||||
# push views to edges
|
||||
ast = graph_rewrite(graph_rewrite(ast, view_left, name="Main View Left"), view_right, name="Main View Right")
|
||||
# replace buffer with define_global + add load/store last
|
||||
bufs = []
|
||||
for s in k.src:
|
||||
s = s.buf_uop
|
||||
# traverse back through MSELECT and MSTACK. HACK: 0 branch of MSTACK only
|
||||
while s.op in {Ops.MSELECT, Ops.MSTACK}: s = s.src[0]
|
||||
bufs.append(s)
|
||||
ast = graph_rewrite(ast, view_left+add_buffer_ops+fix_kernel_ops, bufs, bottom_up=True, name="replace buffer")
|
||||
if ast.op is Ops.SINK and not all_same([x.device for x in k.src]):
|
||||
raise RuntimeError(f"all buffers must be on the same device: {tuple(b.buf_uop.buffer for b in k.src)}")
|
||||
return k.replace(arg=Kernel(ast, k.arg.metadata))
|
||||
|
||||
create_ast = PatternMatcher([(UPat(Ops.KERNEL, name="k"), fix_kernel_ast),])
|
||||
|
||||
# ** add metadata of KERNEL outputs
|
||||
|
||||
def append_metadata(root:UOp, k:UOp):
|
||||
if not root.metadata or (new_metadata:=tuple(dedup(k.arg.metadata+root.metadata))) == k.arg.metadata: return None
|
||||
return root.replace(src=(root.src[0], k.replace(arg=Kernel(k.arg.ast, new_metadata)))+root.src[2:])
|
||||
|
||||
replace_metadata = PatternMatcher([(UPat(Ops.ASSIGN, src=(UPat(), UPat(Ops.KERNEL, name="k")), name="root", allow_any_len=True), append_metadata),])
|
||||
|
||||
pm_fuse = PatternMatcher([
|
||||
# FUSE on CONTIGUOUS removes FUSE
|
||||
(UPat(Ops.CONTIGUOUS, name="c").fuse(), lambda c: c),
|
||||
|
||||
# FUSE triggers swizzle on reduceop
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r").or_casted(),), name="view").fuse(),
|
||||
lambda r,src,view: ret.cast(view.dtype) if (ret:=swizzle_reduceop(r, src, view, fuse=True)) is not None else None),
|
||||
|
||||
# FUSE on reduce (without view) adds fuse marker to grouper
|
||||
(UPat(Ops.REDUCE_AXIS, name="r").fuse(),
|
||||
lambda r: r.replace(src=(r.src[0].fuse(),), arg=r.arg+(True,)) if len(r.arg) == 2 else None),
|
||||
|
||||
# remove FUSE and insert CONTIGUOUS if it's an unsafe pad
|
||||
(UPat(Ops.VIEW, src=(UPat(GroupOp.UnsafePad, name="alu"),), name="view").fuse(),
|
||||
lambda alu, view: alu.contiguous().view(view.st) if any(v.mask is not None for v in view.st.views) else None),
|
||||
|
||||
# FUSE elementwise.
|
||||
(UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST}, name="alu"),), name="view").fuse(),
|
||||
lambda alu, view: alu.replace(src=tuple(apply_swizzle(x.view(view.arg)).fuse() for x in alu.src))),
|
||||
|
||||
# push FUSE through to srcs
|
||||
(UPat(Ops.FUSE, name="x"), lambda x: x.src[0].replace(src=tuple(y.fuse() for y in x.src[0].src))),
|
||||
])
|
||||
|
||||
def do_fusion(x:UOp):
|
||||
found_contiguous = {}
|
||||
def gate_contiguous(x):
|
||||
if is_contiguous:=(x.op is Ops.CONTIGUOUS): found_contiguous[x] = x.replace(src=(UOp(Ops.VIEW, arg=x.st),))
|
||||
return not is_contiguous
|
||||
x.toposort(gate=gate_contiguous)
|
||||
del gate_contiguous
|
||||
return graph_rewrite(x.substitute(found_contiguous), pm_fuse, name="local fusion").substitute({v:k for k,v in found_contiguous.items()})
|
||||
|
||||
def fuse_arange(root:UOp):
|
||||
# skip if root is arange
|
||||
if not FUSE_ARANGE or root.src[0].base.op is Ops.CONST: return None
|
||||
# gather all local aranges (including any fused ones)
|
||||
local_arange: list[UOp] = []
|
||||
def gate_reduce(u):
|
||||
if u.op is Ops.REDUCE_AXIS and u.src[0].base.op is Ops.CONST: local_arange.append(u)
|
||||
return u.op not in {*ALWAYS_CONTIGUOUS, Ops.REDUCE_AXIS} or u is root
|
||||
toposort = root.toposort(gate=gate_reduce)
|
||||
if not local_arange: return None
|
||||
# fuse the nearest expand child of arange
|
||||
local_children: dict[UOp, list[UOp]] = {}
|
||||
for u in toposort:
|
||||
for s in u.src: local_children.setdefault(s, []).append(u)
|
||||
fuse_rep: dict[UOp, UOp] = {}
|
||||
# skip if root depends on aranges with different ndims. This can be improved
|
||||
if any(len(set(dims)) > 1 for dims in zip(*[r.src[0].shape for r in local_arange])): return
|
||||
for r in local_arange:
|
||||
# skip if already fused
|
||||
if len(r.arg) > 2: continue
|
||||
q = list(local_children[r])
|
||||
while q:
|
||||
u = q.pop()
|
||||
if not (curr_children:=local_children.get(u, [])): continue
|
||||
for child in curr_children:
|
||||
other_paths = {s for s in child.toposort() if s.op in {Ops.REDUCE_AXIS, Ops.BUFFER} and s not in {root, r}}
|
||||
fuse_rep[child] = child.replace(src=tuple(s.fuse() if s is u else s for s in child.src))
|
||||
if other_paths: break
|
||||
else: q.extend(curr_children)
|
||||
return root.substitute(fuse_rep, name="fuse_arange") if fuse_rep else None
|
||||
|
||||
do_fuse = PatternMatcher([
|
||||
(UPat(Ops.FUSE, name="x"), do_fusion),
|
||||
(UPat(Ops.REDUCE_AXIS, name="root"), fuse_arange),
|
||||
])
|
||||
|
||||
add_gbarrier = PatternMatcher([(UPat(GroupOp.All-{Ops.GBARRIER, Ops.ASSIGN}, name="x"),
|
||||
lambda ctx,x: x.replace(tag=1).gbarrier() if x in ctx and x.tag is None else None)])
|
||||
|
||||
# TODO: get this from the device through GrouperOpts
|
||||
DEVICE_MAX_BUFS = {"METAL":32, "WEBGPU":8}
|
||||
|
||||
def limit_bufs(root:UOp):
|
||||
# check if backend has a buffer limit
|
||||
device = root.device if isinstance(root.device, str) else root.device[0].split(":")[0]
|
||||
if not (MAX_BUFS:=getenv("MAX_KERNEL_BUFFERS", DEVICE_MAX_BUFS.get(device, 0))): return None
|
||||
# count number of unique buffers flowing into this op
|
||||
bufs: set[UOp] = set()
|
||||
def gate_input(u:UOp):
|
||||
if (is_load:=(u.op in {Ops.BUFFER, Ops.GBARRIER, Ops.ASSIGN, Ops.MSTACK})): bufs.add(u)
|
||||
return not is_load
|
||||
root.toposort(gate=gate_input)
|
||||
# NOTE: this -1 is for the output buffer
|
||||
if len(bufs)>=MAX_BUFS-1:
|
||||
return root.replace(src=tuple(s if s.base in bufs else s.replace(tag=1).gbarrier() for s in root.src))
|
||||
|
||||
finalize_gbarrier = PatternMatcher([
|
||||
# if an op takes more than one input, check combined LOADs don't exceed device limits
|
||||
(UPat(set.union(GroupOp.Binary, GroupOp.Ternary), name="root"), limit_bufs),
|
||||
# merge gbarrier
|
||||
(UPat((Ops.GBARRIER, Ops.CONTIGUOUS), src=(UPat(Ops.GBARRIER),), name="x"), lambda x: x.src[0]),
|
||||
# add contiguous to VIEW before GBARRIER
|
||||
(UPat(Ops.GBARRIER, src=(UPat(Ops.VIEW,),), name="x"), lambda x: x.src[0].contiguous().gbarrier()),
|
||||
# remove gbarrier on constants without a contiguous
|
||||
(UPat(Ops.GBARRIER, src=(UPat(Ops.CONST),), name="x"), lambda x: x.src[0]),
|
||||
])
|
||||
|
||||
remove_tags = PatternMatcher([(UPat(GroupOp.All, name="x"), lambda x: x.replace(tag=None) if x.tag is not None else None)])
|
||||
|
||||
@track_rewrites(name_fxn=lambda big_sink,ret: f"Schedule {pluralize('Kernel',len([u for u in ret[big_sink].toposort() if u.op is Ops.KERNEL]))}")
|
||||
def get_kernelize_map(big_sink:UOp) -> dict[UOp, UOp]:
|
||||
# multi + merge_views + simplify
|
||||
tensor_map = graph_rewrite_map(big_sink, multi_pm+do_fuse+merge_views+sym+replace_contiguous, ctx={}, name="merge_views")
|
||||
|
||||
# display the cleaned up tensor graph
|
||||
if getenv("VIZ"): graph_rewrite(tensor_map[big_sink], PatternMatcher([]), name="View Tensor Graph")
|
||||
|
||||
# insert gbarriers in places determined by the realize map
|
||||
realize_map = group_realizes(tensor_map[big_sink])
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], add_gbarrier, realize_map, bottom_up=True, input_map=tensor_map, name="insert_gbarrier")
|
||||
# optionally reorder gbarriers or insert more (top down)
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], finalize_gbarrier, input_map=tensor_map, name="finalize_gbarrier")
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], remove_tags, input_map=tensor_map, name="remove_tags")
|
||||
|
||||
# TODO: move view_left/view_right here
|
||||
|
||||
# group into kernels (this is context-free)
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], create_kernels, input_map=tensor_map, name="create_kernels")
|
||||
|
||||
# if a kernel depends on a buffer, and that buffer is later assigned to, make the assign depend on the kernel's assign
|
||||
kernel_assign: dict[UOp, UOp] = {}
|
||||
assign_rep: dict[UOp, UOp] = {}
|
||||
for u in tensor_map[big_sink].toposort():
|
||||
if u.op is not Ops.ASSIGN: continue
|
||||
kernel_assign[u.buf_uop] = u
|
||||
for s in u.src[1].src:
|
||||
# TODO: this is probably broken for MSELECT/MSTACK
|
||||
if s.op is not Ops.BUFFER or s is u.buf_uop or (a:=kernel_assign.get(s)) is None: continue
|
||||
if any(x.op is Ops.ASSIGN and x.buf_uop is s for x in u.toposort()):
|
||||
raise RuntimeError(f"cycle detected in graph, kernel for {u.buf_uop} must either depend on ASSIGN or BUFFER")
|
||||
assign_rep[a] = kernel_assign[s] = a.replace(src=a.src+(u,))
|
||||
if assign_rep:
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], _substitute, ctx=assign_rep, bottom_up=True, input_map=tensor_map, name="fix_assign")
|
||||
|
||||
# finally, create the AST for kernels
|
||||
tensor_map = graph_rewrite_map(tensor_map[big_sink], create_ast+replace_metadata, bottom_up=True, input_map=tensor_map, name="create_ast")
|
||||
|
||||
# display the final graph
|
||||
sched_sink = tensor_map[big_sink]
|
||||
if getenv("VIZ"): graph_rewrite(sched_sink, PatternMatcher([]), name="View Kernel Graph")
|
||||
if getenv("VIZ"): graph_rewrite(sched_sink, PatternMatcher([]), name="View Memory Graph")
|
||||
|
||||
# verify Kernels match the spec
|
||||
if __debug__: type_verify(list(sched_sink.toposort()), tensor_uop_spec)
|
||||
|
||||
return tensor_map
|
||||
@@ -14,7 +14,7 @@ from tinygrad.device import Device, Buffer
|
||||
from tinygrad.engine.realize import run_schedule
|
||||
from tinygrad.engine.memory import memory_planner
|
||||
from tinygrad.engine.schedule import ScheduleItem, create_schedule_with_vars
|
||||
from tinygrad.engine.grouper import get_kernelize_map
|
||||
from tinygrad.engine.kernelize import get_kernelize_map
|
||||
|
||||
# *** all in scope Tensors are here. this gets relevant UOps ***
|
||||
|
||||
|
||||
Reference in New Issue
Block a user